Mathematics and Statistics Faculty Publications

Empirical Likelihood Confidence Regions in QVF Measurement Error Models

Document Type

Article

Abstract

In applications in many areas, the data sets are contaminated or corrupted by the mismeasured covariate variables resulting in the so-called measurement errors. In this article, we propose an empirical likelihood method for constructing confidence regions in measurement error models. The quasi variance functions are used to establish functional constraints for the empirical likelihood method. The proposed empirical likelihood confidence regions in measurement error models as a nonparametric approach has clear advantages over the parametric model approaches. The distribution of the response error as well as the distribution of the error prone covariates does not need to be specified. Simulation studies in a simple linear model and a logit regression model are conducted. The results show that the proposed method performs well.

Publication Date

2-2015

Publication Title

Journal of Probability and Statistical Science

Volume

13

Issue

1

Start Page No.

25

End Page No.

34

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